diff --git a/tensorflow/python/keras/optimizer_v2/optimizer_v2.py b/tensorflow/python/keras/optimizer_v2/optimizer_v2.py
index 8569f55d164..c6e1d57c5e4 100644
--- a/tensorflow/python/keras/optimizer_v2/optimizer_v2.py
+++ b/tensorflow/python/keras/optimizer_v2/optimizer_v2.py
@@ -346,8 +346,26 @@ class OptimizerV2(optimizer_v1.Optimizer):
     value = self._hyper[name]
     return self._call_if_callable(value)
 
+  def __getattribute__(self, name):
+    """Overridden to support hyperparameter access."""
+    try:
+      return super(OptimizerV2, self).__getattribute__(name)
+    except AttributeError as e:
+      # Needed to avoid infinite recursion with __setattr__.
+      if name == "_hyper":
+        raise e
+      # Backwards compatibility with Keras optimizers.
+      if name == "lr":
+        name = "learning_rate"
+      if name in self._hyper:
+        return self._hyper[name]
+      raise e
+
   def __setattr__(self, name, value):
     """Override setattr to support dynamic hyperparameter setting."""
+    # Backwards compatibility with Keras optimizers.
+    if name == "lr":
+      name = "learning_rate"
     if hasattr(self, "_hyper") and name in self._hyper:
       self._set_hyper(name, value)
     else:
diff --git a/tensorflow/python/keras/optimizer_v2/optimizer_v2_test.py b/tensorflow/python/keras/optimizer_v2/optimizer_v2_test.py
index e5d1a104ca4..682deda23f0 100644
--- a/tensorflow/python/keras/optimizer_v2/optimizer_v2_test.py
+++ b/tensorflow/python/keras/optimizer_v2/optimizer_v2_test.py
@@ -370,6 +370,30 @@ class OptimizerTest(test.TestCase):
       self.assertAllClose(
           self.evaluate([var3, var4]), self.evaluate([var5, var6]))
 
+  @test_util.run_in_graph_and_eager_modes
+  def testGettingHyperParameters(self):
+    opt = adam.Adam(learning_rate=1.0)
+    var = resource_variable_ops.ResourceVariable([1.0, 2.0],
+                                                 dtype=dtypes.float32)
+    loss = lambda: 3 * var
+    opt_op = opt.minimize(loss, [var])
+    self.evaluate(variables.global_variables_initializer())
+    self.evaluate(opt_op)
+
+    lr = self.evaluate(opt.lr)
+    self.assertEqual(1.0, lr)
+
+    opt.lr = 2.0
+    lr = self.evaluate(opt.lr)
+    self.assertEqual(2.0, lr)
+
+    self.evaluate(opt.lr.assign(3.0))
+    lr = self.evaluate(opt.lr)
+    self.assertEqual(3.0, lr)
+
+    with self.assertRaises(AttributeError):
+      opt.not_an_attr += 3
+
   def testOptimizerWithFunction(self):
     with context.eager_mode():
       var = resource_variable_ops.ResourceVariable([1.0, 2.0],